Diverse role of NMDA receptors for dendritic integration of neural dynamics.

Neurons, represented as a tree structure of morphology, have various distinguished branches of dendrites. Different types of synaptic receptors distributed over dendrites are responsible for receiving inputs from other neurons. NMDA receptors (NMDARs) are expressed as excitatory units, and play a key physiological role in synaptic function. Although NMDARs are widely expressed in most types of neurons, they play a different role in the cerebellar Purkinje cells (PCs). Utilizing a computational PC model with detailed dendritic morphology, we explored the role of NMDARs at different parts of dendritic branches and regions. We found somatic responses can switch from silent, to simple spikes and complex spikes, depending on specific dendritic branches. Detailed examination of the dendrites regarding their diameters and distance to soma revealed diverse response patterns, yet explain two firing modes, simple and complex spike. Taken together, these results suggest that NMDARs play an important role in controlling excitability sensitivity while taking into account the factor of dendritic properties. Given the complexity of neural morphology varying in cell types, our work suggests that the functional role of NMDARs is not stereotyped but highly interwoven with local properties of neuronal structure.

Nrf2 signaling in heart failure: expression of Nrf2, Keap1, antioxidant, and detoxification genes in dilated or ischemic cardiomyopathy.

Increased levels of oxidative stress have been found with heart failure. Whether failing hearts express antioxidant and detoxification enzymes have not been addressed systematically. Nrf2 gene encodes a transcription factor that regulates the expression of antioxidant and detoxification genes. Using RNA-Seq data set from explanted hearts of 37 patients with dilated cardiomyopathy (DCM), 13 patients with ischemic cardiomyopathy (ICM), and 14 nonfailure (NF) donors as a control, we addressed whether failing hearts change the expression of Nrf2, its negative regulator Keap1, and antioxidant or detoxification genes. Significant increases in the ratio of Nrf2 to Keap1 were found to associate with DCM or ICM. Antioxidant genes showed decreased expression in both types of heart failure, including NQO1, SOD1, GPX3, GPX4, GSR, PRDX1, and TXNRD1. Detoxification enzymes, GCLM and EPHX1, also showed decreased expression, whereas the CYP1B1 transcript was elevated in both DCM and ICM. The genes encoding metal-binding protein ferritin were decreased, whereas five out of 12 metallothionein genes showed elevated expression. Our finding on Nrf2 gene expression has been validated by meta-analysis of seven independent data sets of microarray or RNA-Seq for differential gene expression in DCM and ICM from NF controls. In conclusion, minor elevation of Nrf2 gene expression is not coupled to increases in antioxidant and detoxification genes, supporting an impairment of Nrf2 signaling in patients with heart failure. Decreases in multiple antioxidant and detoxification genes are consistent with the observed increases of oxidative stress in failing hearts.

Regulating synchronous oscillations of cerebellar granule cells by different types of inhibition.

Synchronous oscillations in neural populations are considered being controlled by inhibitory neurons. In the granular layer of the cerebellum, two major types of cells are excitatory granular cells (GCs) and inhibitory Golgi cells (GoCs). GC spatiotemporal dynamics, as the output of the granular layer, is highly regulated by GoCs. However, there are various types of inhibition implemented by GoCs. With inputs from mossy fibers, GCs and GoCs are reciprocally connected to exhibit different network motifs of synaptic connections. From the view of GCs, feedforward inhibition is expressed as the direct input from GoCs excited by mossy fibers, whereas feedback inhibition is from GoCs via GCs themselves. In addition, there are abundant gap junctions between GoCs showing another form of inhibition. It remains unclear how these diverse copies of inhibition regulate neural population oscillation changes. Leveraging a computational model of the granular layer network, we addressed this question to examine the emergence and modulation of network oscillation using different types of inhibition. We show that at the network level, feedback inhibition is crucial to generate neural oscillation. When short-term plasticity was equipped on GoC-GC synapses, oscillations were largely diminished. Robust oscillations can only appear with additional gap junctions. Moreover, there was a substantial level of cross-frequency coupling in oscillation dynamics. Such a coupling was adjusted and strengthened by GoCs through feedback inhibition. Taken together, our results suggest that the cooperation of distinct types of GoC inhibition plays an essential role in regulating synchronous oscillations of the GC population. With GCs as the sole output of the granular network, their oscillation dynamics could potentially enhance the computational capability of downstream neurons.

Modulation of the dynamics of cerebellar Purkinje cells through the interaction of excitatory and inhibitory feedforward pathways.

The dynamics of cerebellar neuronal networks is controlled by the underlying building blocks of neurons and synapses between them. For which, the computation of Purkinje cells (PCs), the only output cells of the cerebellar cortex, is implemented through various types of neural pathways interactively routing excitation and inhibition converged to PCs. Such tuning of excitation and inhibition, coming from the gating of specific pathways as well as short-term plasticity (STP) of the synapses, plays a dominant role in controlling the PC dynamics in terms of firing rate and spike timing. PCs receive cascade feedforward inputs from two major neural pathways: the first one is the feedforward excitatory pathway from granule cells (GCs) to PCs; the second one is the feedforward inhibition pathway from GCs, via molecular layer interneurons (MLIs), to PCs. The GC-PC pathway, together with short-term dynamics of excitatory synapses, has been a focus over past decades, whereas recent experimental evidence shows that MLIs also greatly contribute to controlling PC activity. Therefore, it is expected that the diversity of excitation gated by STP of GC-PC synapses, modulated by strong inhibition from MLI-PC synapses, can promote the computation performed by PCs. However, it remains unclear how these two neural pathways are interacted to modulate PC dynamics. Here using a computational model of PC network installed with these two neural pathways, we addressed this question to investigate the change of PC firing dynamics at the level of single cell and network. We show that the nonlinear characteristics of excitatory STP dynamics can significantly modulate PC spiking dynamics mediated by inhibition. The changes in PC firing rate, firing phase, and temporal spike pattern, are strongly modulated by these two factors in different ways. MLIs mainly contribute to variable delays in the postsynaptic action potentials of PCs while modulated by excitation STP. Notably, the diversity of synchronization and pause response in the PC network is governed not only by the balance of excitation and inhibition, but also by the synaptic STP, depending on input burst patterns. Especially, the pause response shown in the PC network can only emerge with the interaction of both pathways. Together with other recent findings, our results show that the interaction of feedforward pathways of excitation and inhibition, incorporated with synaptic short-term dynamics, can dramatically regulate the PC activities that consequently change the network dynamics of the cerebellar circuit.

scDoc: correcting drop-out events in single-cell RNA-seq data.

MOTIVATION: Single-cell RNA-sequencing (scRNA-seq) has become an important tool to unravel cellular heterogeneity, discover new cell (sub)types, and understand cell development at single-cell resolution. However, one major challenge to scRNA-seq research is the presence of 'drop-out' events, which usually is due to extremely low mRNA input or the stochastic nature of gene expression. In this article, we present a novel single-cell RNA-seq drop-out correction (scDoc) method, imputing drop-out events by borrowing information for the same gene from highly similar cells. RESULTS: scDoc is the first method that directly involves drop-out information to accounting for cell-to-cell similarity estimation, which is crucial in scRNA-seq drop-out imputation but has not been appropriately examined. We evaluated the performance of scDoc using both simulated data and real scRNA-seq studies. Results show that scDoc outperforms the existing imputation methods in reference to data visualization, cell subpopulation identification and differential expression detection in scRNA-seq data. AVAILABILITY AND IMPLEMENTATION: R code is available at https://github.com/anlingUA/scDoc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Club Cell Marker SCGB1A1 Downstream of FOXA2 is Reduced in Asthma.

Human SCGB1A1 protein has been shown to be significantly reduced in BAL, sputum, and serum from humans with asthma as compared with healthy individuals. However, the mechanism of this reduction and its functional impact have not been entirely elucidated. By mining online datasets, we found that the mRNA of SCGB1A1 was significantly repressed in brushed human airway epithelial cells from individuals with asthma, and this repression appeared to be associated with reduced expression of FOXA2. Consistently, both Scgb1A1 and FoxA2 were downregulated in an ovalbumin-induced mouse model of asthma. Furthermore, compared with wild-type mice, Scgb1a1 knockout mice had increased airway hyperreactivity and inflammation when they were exposed to ovalbumin, confirming the antiinflammatory role of Scgb1a1 in protection against asthma phenotypes. To search for potential asthma-related stimuli of SCGB1A1 repression, we tested T-helper cell type 2 cytokines. Both IL-4 and IL-13 repressed epithelial expression of SCGB1A1 and FOXA2. Importantly, infection of epithelial cells with human rhinovirus similarly reduced expression of these two genes, which suggests that FOXA2 may be the common regulator of SCGB1A1. To establish the causal role of reduced FOXA2 in SCGB1A1 repression, we demonstrated that FOXA2 was required for SCGB1A1 expression at baseline. FOXA2 overexpression was sufficient to drive promoter activity and expression of SCGB1A1 and was also able to restore the repressed SCGB1A1 expression in IL-13-treated or rhinovirus-infected cells. Taken together, these findings suggest that low levels of epithelial SCGB1A1 in asthma are caused by reduced FOXA2 expression.

FERTILIZATION-INDEPENDENT SEED-Polycomb Repressive Complex 2 Plays a Dual Role in Regulating Type I MADS-Box Genes in Early Endosperm Development.

Early endosperm development presents a unique system in which to uncover epigenetic regulatory mechanisms because the contributing maternal and paternal genomes possess differential epigenetic modifications. In Arabidopsis (Arabidopsis thaliana), the initiation of endosperm coenocytic growth upon fertilization and the transition to endosperm cellularization are regulated by the FERTILIZATION-INDEPENDENT SEED (FIS)-Polycomb Repressive Complex 2 (PRC2), a putative H3K27 methyltransferase. Here, we address the possible role of the FIS-PRC2 complex in regulating the type I MADS-box gene family, which has been shown previously to regulate early endosperm development. We show that a subclass of type I MADS-box genes (C2 genes) was expressed in distinct domains of the coenocytic endosperm in wild-type seeds. Furthermore, the C2 genes were mostly up-regulated biallelically during the extended coenocytic phase of endosperm development in the FIS-PRC2 mutant background. Using allele-specific expression analysis, we also identified a small subset of C2 genes subjected to FIS-PRC2-dependent maternal or FIS-PRC2-independent paternal imprinting. Our data support a dual role for the FIS-PRC2 complex in the regulation of C2 type I MADS-box genes, as evidenced by a generalized role in the repression of gene expression at both alleles associated with endosperm cellularization and a specialized role in silencing the maternal allele of imprinted genes.

An informative approach on differential abundance analysis for time-course metagenomic sequencing data.

Motivation: The advent of high-throughput next generation sequencing technology has greatly promoted the field of metagenomics where previously unattainable information about microbial communities can be discovered. Detecting differentially abundant features (e.g. species or genes) plays a critical role in revealing the contributors (i.e. pathogens) to the biological or medical status of microbial samples. However, currently available statistical methods lack power in detecting differentially abundant features contrasting different biological or medical conditions, in particular, for time series metagenomic sequencing data. We have proposed a novel procedure, metaDprof, which is built upon a spline-based method assuming heterogeneous error, to meet the challenges of detecting differentially abundant features from metagenomic samples by comparing different biological/medical conditions across time. It contains two stages: (i) global detection on features and (ii) time interval detection for significant features. The detection procedures in both stages are based on sound statistical support. Results: Compared with existing methods the new method metaDprof shows the best performance in comprehensive simulation studies. Not only can it accurately detect features relating to the biological condition or disease status of samples but it also can accurately detect the starting and ending time points when the differences arise. The proposed method is also applied to a real metagenomic dataset and the results provide an interesting angle to understand the relationship between the microbiota in mouse gut and diet type. Availability and Implementation: R code and an example dataset are available at https://cals.arizona.edu/∼anling/sbg/software.htm. Contact: anling@email.arizona.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Model Selection and Estimation with Quantal-Response Data in Benchmark Risk Assessment.

This article describes several approaches for estimating the benchmark dose (BMD) in a risk assessment study with quantal dose-response data and when there are competing model classes for the dose-response function. Strategies involving a two-step approach, a model-averaging approach, a focused-inference approach, and a nonparametric approach based on a PAVA-based estimator of the dose-response function are described and compared. Attention is raised to the perils involved in data "double-dipping" and the need to adjust for the model-selection stage in the estimation procedure. Simulation results are presented comparing the performance of five model selectors and eight BMD estimators. An illustration using a real quantal-response data set from a carcinogenecity study is provided.

Investigating microbial co-occurrence patterns based on metagenomic compositional data.

MOTIVATION: The high-throughput sequencing technologies have provided a powerful tool to study the microbial organisms living in various environments. Characterizing microbial interactions can give us insights into how they live and work together as a community. Metagonomic data are usually summarized in a compositional fashion due to varying sampling/sequencing depths from one sample to another. We study the co-occurrence patterns of microbial organisms using their relative abundance information. Analyzing compositional data using conventional correlation methods has been shown prone to bias that leads to artifactual correlations. RESULTS: We propose a novel method, regularized estimation of the basis covariance based on compositional data (REBACCA), to identify significant co-occurrence patterns by finding sparse solutions to a system with a deficient rank. To be specific, we construct the system using log ratios of count or proportion data and solve the system using the l1-norm shrinkage method. Our comprehensive simulation studies show that REBACCA (i) achieves higher accuracy in general than the existing methods when a sparse condition is satisfied; (ii) controls the false positives at a pre-specified level, while other methods fail in various cases and (iii) runs considerably faster than the existing comparable method. REBACCA is also applied to several real metagenomic datasets. AVAILABILITY AND IMPLEMENTATION: The R codes for the proposed method are available at http://faculty.wcas.northwestern.edu/∼hji403/REBACCA.htm CONTACT: hongmei@northwestern.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A robust approach for identifying differentially abundant features in metagenomic samples.

MOTIVATION: The analysis of differential abundance for features (e.g. species or genes) can provide us with a better understanding of microbial communities, thus increasing our comprehension and understanding of the behaviors of microbial communities. However, it could also mislead us about the characteristics of microbial communities if the abundances or counts of features on different scales are not properly normalized within and between communities, prior to the analysis of differential abundance. Normalization methods used in the differential analysis typically try to adjust counts on different scales to a common scale using the total sum, mean or median of representative features across all samples. These methods often yield undesirable results when the difference in total counts of differentially abundant features (DAFs) across different conditions is large. RESULTS: We develop a novel method, Ratio Approach for Identifying Differential Abundance (RAIDA), which utilizes the ratio between features in a modified zero-inflated lognormal model. RAIDA removes possible problems associated with counts on different scales within and between conditions. As a result, its performance is not affected by the amount of difference in total abundances of DAFs across different conditions. Through comprehensive simulation studies, the performance of our method is consistently powerful, and under some situations, RAIDA greatly surpasses other existing methods. We also apply RAIDA on real datasets of type II diabetes and find interesting results consistent with previous reports. AVAILABILITY AND IMPLEMENTATION: An R package for RAIDA can be accessed from http://cals.arizona.edu/%7Eanling/sbg/software.htm.

A two-stage statistical procedure for feature selection and comparison in functional analysis of metagenomes.

MOTIVATION: With the advance of new sequencing technologies producing massive short reads data, metagenomics is rapidly growing, especially in the fields of environmental biology and medical science. The metagenomic data are not only high dimensional with large number of features and limited number of samples but also complex with a large number of zeros and skewed distribution. Efficient computational and statistical tools are needed to deal with these unique characteristics of metagenomic sequencing data. In metagenomic studies, one main objective is to assess whether and how multiple microbial communities differ under various environmental conditions. RESULTS: We propose a two-stage statistical procedure for selecting informative features and identifying differentially abundant features between two or more groups of microbial communities. In the functional analysis of metagenomes, the features may refer to the pathways, subsystems, functional roles and so on. In the first stage of the proposed procedure, the informative features are selected using elastic net as reducing the dimension of metagenomic data. In the second stage, the differentially abundant features are detected using generalized linear models with a negative binomial distribution. Compared with other available methods, the proposed approach demonstrates better performance for most of the comprehensive simulation studies. The new method is also applied to two real metagenomic datasets related to human health. Our findings are consistent with those in previous reports. AVAILABILITY: R code and two example datasets are available at http://cals.arizona.edu/∼anling/software.htm. SUPPLEMENTARY INFORMATION: Supplementary file is available at Bioinformatics online.

Complement C5a potentiates uric acid crystal-induced IL-1ß production.

Anaphylatoxin C5a released upon complement activation is associated with both acute and chronic inflammations such as gout. The pathogenesis of gout was identified as uric acid crystal deposition in the joints that activates inflammasome, leading to IL-1ß release. However, little is known about the interaction between complement activation and monosodium urate/uric acid (MSU) crystal-induced inflammasome activation or IL-1ß production. Here, we report that MSU crystal-induced proinflammatory cytokines/chemokines in human whole blood is predominantly regulated by C5a through its interaction with C5a receptor. C5a induces pro-IL-1ß and IL-1ß production in human primary monocytes, and potentiates MSU or cholesterol crystals in IL-1ß production. This potentiation is caspase-1 dependent and requires intracellular Ca(2+) mobilization, K(+) efflux, and cathepsin B activity. Our results provide insight into the role of C5a as an endogenous priming signal that is required for the initiation of uric acid crystal-induced IL-1ß production. C5a could potentially be a therapeutic target together with IL-1ß antagonists for the treatment of complement-dependent and inflammasome-associated diseases.

Highly Segregated Lamello-Columnar Mesophase Organizations and Fast Charge Carrier Mobility in New Discotic Donor-Acceptor Triads.

Four new donor-acceptor triads (D-A-D) based on discotic and arylene mesogens have been synthesized by using Sonogashira coupling and cyclization reactions. This family of triads consists of two side-on pending triphenylene mesogens, acting as the electron-donating groups (D), laterally connected through short lipophilic spacers to a central perylenediimide (PI), benzo[ghi]perylenediimide (BI), or coronenediimide (CI) molecular unit, respectively, playing the role of the electron acceptor (A). All D-A-D triads self-organize to form a lamello-columnar oblique mesophase, with a highly segregated donor-acceptor (D-A) heterojunction organization, consequent to efficient molecular self-sorting. The structure consists in the regular alternation of two disrupted rows of triphenylene columns and a continuous row of diimine species. High-resolution STM images demonstrate that PI-TP2 forms stable 2D self-assembly nanostructures with some various degrees of regularity, whereas the other triads do not self-organize into ordered architectures. The electron-transport mobility of CI-TP2, measured by time-of-flight at 200 °C in the mesophase, is one order of magnitude higher than the hole mobility. By means of this specific molecular designing idea, we realized and demonstrated for the first time the so-called p-n heterojunction at the molecular level in which the electron-rich triphenylene columns act as the hole transient pathways, and the coronenediimide stacks form the electron-transport channels.

Pharmacological inhibition of type I interferon signaling protects mice against lethal sepsis.

Current research on new therapeutic strategies for sepsis uses different animal models, such as the lipopolysaccharide-induced endotoxemia model and the cecal ligation and puncture (CLP) peritonitis model. By using genetic and pharmacologic inhibition of the type I interferon (IFN) receptor (IFNAR1), we show that type I IFN signaling plays a detrimental role in these sepsis models. Mortality after CLP was reduced even when type I IFN responses were blocked after the onset of sepsis. Our findings reveal that type I IFNs play an important detrimental role during sepsis by negatively regulating neutrophil recruitment. Reduced neutrophil influx likely occurs via the induction of the CXC motif chemokine 1. Moreover, human white blood cells exposed to heat-killed Pseudomonas aeruginosa secrete IFN-ß and stimulate type I IFN signaling. We provide data that support pharmacologic inhibition of type I IFN signaling as a novel therapeutic treatment in severe sepsis.

Accurate genome relative abundance estimation for closely related species in a metagenomic sample.

BACKGROUND: Metagenomics has a great potential to discover previously unattainable information about microbial communities. An important prerequisite for such discoveries is to accurately estimate the composition of microbial communities. Most of prevalent homology-based approaches utilize solely the results of an alignment tool such as BLAST, limiting their estimation accuracy to high ranks of the taxonomy tree. RESULTS: We developed a new homology-based approach called Taxonomic Analysis by Elimination and Correction (TAEC), which utilizes the similarity in the genomic sequence in addition to the result of an alignment tool. The proposed method is comprehensively tested on various simulated benchmark datasets of diverse complexity of microbial structure. Compared with other available methods designed for estimating taxonomic composition at a relatively low taxonomic rank, TAEC demonstrates greater accuracy in quantification of genomes in a given microbial sample. We also applied TAEC on two real metagenomic datasets, oral cavity dataset and Crohn's disease dataset. Our results, while agreeing with previous findings at higher ranks of the taxonomy tree, provide accurate estimation of taxonomic compositions at the species/strain level, narrowing down which species/strains need more attention in the study of oral cavity and the Crohn's disease. CONCLUSIONS: By taking account of the similarity in the genomic sequence TAEC outperforms other available tools in estimating taxonomic composition at a very low rank, especially when closely related species/strains exist in a metagenomic sample.

Class I lysine deacetylases facilitate glucocorticoid-induced transcription.

Nuclear receptors use lysine acetyltransferases and lysine deacetylases (KDACs) in regulating transcription through histone acetylation. Lysine acetyltransferases interact with steroid receptors upon binding of an agonist and are recruited to target genes. KDACs have been shown to interact with steroid receptors upon binding to an antagonist. We have shown previously that KDAC inhibitors (KDACis) potently repress the mouse mammary tumor virus promoter through transcriptional mechanisms and impair the ability of the glucocorticoid receptor (GR) to activate it, suggesting that KDACs can play a positive role in GR transactivation. In the current study, we extended this analysis to the entire GR transcriptome and found that the KDACi valproic acid impairs the ability of agonist-bound GR to activate about 50% of its target genes. This inhibition is largely due to impaired transcription rather than defective GR processing and was also observed using a structurally distinct KDACi. Depletion of KDAC1 expression mimicked the effects of KDACi in over half of the genes found to be impaired in GR transactivation. Simultaneous depletion of KDACs 1 and 2 caused full or partial impairment of several more GR target genes. Altogether we found that Class I KDAC activity facilitates GR-mediated activation at a sizable fraction of GR-activated target genes and that KDAC1 alone or in coordination with KDAC2 is required for efficient GR transactivation at many of these target genes. Finally, our work demonstrates that KDACi exposure has a significant impact on GR signaling and thus has ramifications for the clinical use of these drugs.

SPADE: spatial deconvolution for domain specific cell-type estimation.

Understanding gene expression in different cell types within their spatial context is a key goal in genomics research. SPADE (SPAtial DEconvolution), our proposed method, addresses this by integrating spatial patterns into the analysis of cell type composition. This approach uses a combination of single-cell RNA sequencing, spatial transcriptomics, and histological data to accurately estimate the proportions of cell types in various locations. Our analyses of synthetic data have demonstrated SPADE's capability to discern cell type-specific spatial patterns effectively. When applied to real-life datasets, SPADE provides insights into cellular dynamics and the composition of tumor tissues. This enhances our comprehension of complex biological systems and aids in exploring cellular diversity. SPADE represents a significant advancement in deciphering spatial gene expression patterns, offering a powerful tool for the detailed investigation of cell types in spatial transcriptomics.

Information-theoretic model-averaged benchmark dose analysis in environmental risk assessment.

An important objective in environmental risk assessment is estimation of minimum exposure levels, called Benchmark Doses (BMDs), that induce a pre-specified Benchmark Response (BMR) in a dose-response experiment. In such settings, representations of the risk are traditionally based on a specified parametric model. It is a well-known concern, however, that existing parametric estimation techniques are sensitive to the form employed for modeling the dose response. If the chosen parametric model is in fact misspecified, this can lead to inaccurate low-dose inferences. Indeed, avoiding the impact of model selection was one early motivating issue behind development of the BMD technology. Here, we apply a frequentist model averaging approach for estimating benchmark doses, based on information-theoretic weights. We explore how the strategy can be used to build one-sided lower confidence limits on the BMD, and we study the confidence limits' small-sample properties via a simulation study. An example from environmental carcinogenicity testing illustrates the calculations. It is seen that application of this information-theoretic, model averaging methodology to benchmark analysis can improve environmental health planning and risk regulation when dealing with low-level exposures to hazardous agents.

Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics.

Single-cell data analysis has been at forefront of development in biology and medicine since sequencing data have been made available. An important challenge in single-cell data analysis is the identification of cell types. Several methods have been proposed for cell-type identification. However, these methods do not capture the higher-order topological relationship between different samples. In this work, we propose an attention-based graph neural network that captures the higher-order topological relationship between different samples and performs transductive learning for predicting cell types. The evaluation of our method on both simulation and publicly available datasets demonstrates the superiority of our method, scAGN, in terms of prediction accuracy. In addition, our method works best for highly sparse datasets in terms of F1 score, precision score, recall score, and Matthew's correlation coefficients as well. Further, our method's runtime complexity is consistently faster compared to other methods.